Overview

Dataset statistics

Number of variables14
Number of observations505
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.4 KiB
Average record size in memory112.3 B

Variable types

Numeric13
Categorical1

Alerts

0.00632 is highly overall correlated with 0.5380 and 8 other fieldsHigh correlation
0.5380 is highly overall correlated with 0.00632 and 8 other fieldsHigh correlation
1 is highly overall correlated with 0.00632 and 2 other fieldsHigh correlation
15.30 is highly overall correlated with 24.00High correlation
18.00 is highly overall correlated with 0.00632 and 4 other fieldsHigh correlation
2.310 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
24.00 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
296.0 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
4.0900 is highly overall correlated with 0.00632 and 6 other fieldsHigh correlation
4.98 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
6.5750 is highly overall correlated with 24.00 and 1 other fieldsHigh correlation
65.20 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
0 is highly imbalanced (63.7%)Imbalance
18.00 has 372 (73.7%) zerosZeros

Reproduction

Analysis started2026-02-22 19:42:26.644697
Analysis finished2026-02-22 19:42:43.282264
Duration16.64 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

0.00632
Real number (ℝ)

High correlation 

Distinct503
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6206665
Minimum0.00906
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:43.391481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00906
5-th percentile0.028798
Q10.08221
median0.25915
Q33.67822
95-th percentile15.80338
Maximum88.9762
Range88.96714
Interquartile range (IQR)3.59601

Descriptive statistics

Standard deviation8.6085718
Coefficient of variation (CV)2.3776207
Kurtosis37.062371
Mean3.6206665
Median Absolute Deviation (MAD)0.22405
Skewness5.2183956
Sum1828.4366
Variance74.107509
MonotonicityNot monotonic
2026-02-23T01:12:43.496719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015012
 
0.4%
14.33372
 
0.4%
0.047411
 
0.2%
0.027311
 
0.2%
0.027291
 
0.2%
0.032371
 
0.2%
0.069051
 
0.2%
0.988431
 
0.2%
8.055791
 
0.2%
6.393121
 
0.2%
Other values (493)493
97.6%
ValueCountFrequency (%)
0.009061
0.2%
0.010961
0.2%
0.013011
0.2%
0.013111
0.2%
0.01361
0.2%
0.013811
0.2%
0.014321
0.2%
0.014391
0.2%
0.015012
0.4%
0.015381
0.2%
ValueCountFrequency (%)
88.97621
0.2%
73.53411
0.2%
67.92081
0.2%
51.13581
0.2%
45.74611
0.2%
41.52921
0.2%
38.35181
0.2%
37.66191
0.2%
28.65581
0.2%
25.94061
0.2%

18.00
Real number (ℝ)

High correlation  Zeros 

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.350495
Minimum0
Maximum100
Zeros372
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:43.592633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.343704
Coefficient of variation (CV)2.0566243
Kurtosis4.0249787
Mean11.350495
Median Absolute Deviation (MAD)0
Skewness2.2256648
Sum5732
Variance544.9285
MonotonicityNot monotonic
2026-02-23T01:12:43.673284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0372
73.7%
2021
 
4.2%
8015
 
3.0%
12.510
 
2.0%
2510
 
2.0%
2210
 
2.0%
407
 
1.4%
456
 
1.2%
306
 
1.2%
905
 
1.0%
Other values (15)43
 
8.5%
ValueCountFrequency (%)
0372
73.7%
12.510
 
2.0%
17.51
 
0.2%
2021
 
4.2%
214
 
0.8%
2210
 
2.0%
2510
 
2.0%
283
 
0.6%
306
 
1.2%
334
 
0.8%
ValueCountFrequency (%)
1001
 
0.2%
954
 
0.8%
905
 
1.0%
852
 
0.4%
82.52
 
0.4%
8015
3.0%
753
 
0.6%
703
 
0.6%
604
 
0.8%
553
 
0.6%

2.310
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.154257
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:43.766892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.8558684
Coefficient of variation (CV)0.6146414
Kurtosis-1.2338757
Mean11.154257
Median Absolute Deviation (MAD)6.32
Skewness0.29276212
Sum5632.9
Variance47.002931
MonotonicityNot monotonic
2026-02-23T01:12:43.855090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1132
26.1%
19.5830
 
5.9%
8.1422
 
4.4%
6.218
 
3.6%
21.8915
 
3.0%
9.912
 
2.4%
3.9712
 
2.4%
8.5611
 
2.2%
10.5911
 
2.2%
5.8610
 
2.0%
Other values (65)232
45.9%
ValueCountFrequency (%)
0.461
 
0.2%
0.741
 
0.2%
1.211
 
0.2%
1.221
 
0.2%
1.252
0.4%
1.321
 
0.2%
1.381
 
0.2%
1.472
0.4%
1.524
0.8%
1.692
0.4%
ValueCountFrequency (%)
27.745
 
1.0%
25.657
 
1.4%
21.8915
 
3.0%
19.5830
 
5.9%
18.1132
26.1%
15.043
 
0.6%
13.925
 
1.0%
13.894
 
0.8%
12.836
 
1.2%
11.935
 
1.0%

0
Categorical

Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
470 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters505
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0470
93.1%
135
 
6.9%

Length

2026-02-23T01:12:43.967850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T01:12:44.052386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0470
93.1%
135
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0470
93.1%
135
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0470
93.1%
135
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0470
93.1%
135
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0470
93.1%
135
 
6.9%

0.5380
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55472812
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:44.133499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.4092
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.11599019
Coefficient of variation (CV)0.20909376
Kurtosis-0.071076233
Mean0.55472812
Median Absolute Deviation (MAD)0.089
Skewness0.7277837
Sum280.1377
Variance0.013453724
MonotonicityNot monotonic
2026-02-23T01:12:44.257437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53822
 
4.4%
0.71318
 
3.6%
0.43717
 
3.4%
0.87116
 
3.2%
0.62415
 
3.0%
0.48915
 
3.0%
0.69314
 
2.8%
0.60514
 
2.8%
0.7413
 
2.6%
0.54412
 
2.4%
Other values (71)349
69.1%
ValueCountFrequency (%)
0.3851
 
0.2%
0.3891
 
0.2%
0.3922
0.4%
0.3941
 
0.2%
0.3982
0.4%
0.44
0.8%
0.4013
0.6%
0.4033
0.6%
0.4043
0.6%
0.4053
0.6%
ValueCountFrequency (%)
0.87116
3.2%
0.778
1.6%
0.7413
2.6%
0.7186
 
1.2%
0.71318
3.6%
0.711
2.2%
0.69314
2.8%
0.6798
1.6%
0.6717
 
1.4%
0.6683
 
0.6%

6.5750
Real number (ℝ)

High correlation 

Distinct445
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2840594
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:44.359753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.312
Q15.885
median6.208
Q36.625
95-th percentile7.592
Maximum8.78
Range5.219
Interquartile range (IQR)0.74

Descriptive statistics

Standard deviation0.70319467
Coefficient of variation (CV)0.11190134
Kurtosis1.8864562
Mean6.2840594
Median Absolute Deviation (MAD)0.344
Skewness0.40574304
Sum3173.45
Variance0.49448274
MonotonicityNot monotonic
2026-02-23T01:12:44.472015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.7133
 
0.6%
6.1273
 
0.6%
6.4173
 
0.6%
6.4053
 
0.6%
6.1673
 
0.6%
6.2293
 
0.6%
6.032
 
0.4%
5.9832
 
0.4%
5.3042
 
0.4%
5.392
 
0.4%
Other values (435)479
94.9%
ValueCountFrequency (%)
3.5611
0.2%
3.8631
0.2%
4.1382
0.4%
4.3681
0.2%
4.5191
0.2%
4.6281
0.2%
4.6521
0.2%
4.881
0.2%
4.9031
0.2%
4.9061
0.2%
ValueCountFrequency (%)
8.781
0.2%
8.7251
0.2%
8.7041
0.2%
8.3981
0.2%
8.3751
0.2%
8.3371
0.2%
8.2971
0.2%
8.2661
0.2%
8.2591
0.2%
8.2471
0.2%

65.20
Real number (ℝ)

High correlation 

Distinct356
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.581584
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:44.588074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.72
Q145
median77.7
Q394.1
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.1

Descriptive statistics

Standard deviation28.176371
Coefficient of variation (CV)0.41084457
Kurtosis-0.97107393
Mean68.581584
Median Absolute Deviation (MAD)19.6
Skewness-0.59911057
Sum34633.7
Variance793.90789
MonotonicityNot monotonic
2026-02-23T01:12:44.705600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10043
 
8.5%
964
 
0.8%
87.94
 
0.8%
98.24
 
0.8%
97.94
 
0.8%
95.44
 
0.8%
98.84
 
0.8%
973
 
0.6%
883
 
0.6%
95.63
 
0.6%
Other values (346)429
85.0%
ValueCountFrequency (%)
2.91
0.2%
61
0.2%
6.21
0.2%
6.51
0.2%
6.62
0.4%
6.81
0.2%
7.82
0.4%
8.41
0.2%
8.91
0.2%
9.81
0.2%
ValueCountFrequency (%)
10043
8.5%
99.31
 
0.2%
99.11
 
0.2%
98.93
 
0.6%
98.84
 
0.8%
98.71
 
0.2%
98.51
 
0.2%
98.42
 
0.4%
98.32
 
0.4%
98.24
 
0.8%

4.0900
Real number (ℝ)

High correlation 

Distinct411
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7944586
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:44.823697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.46174
Q12.1
median3.1992
Q35.2119
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.1119

Descriptive statistics

Standard deviation2.1077571
Coefficient of variation (CV)0.55548295
Kurtosis0.48244716
Mean3.7944586
Median Absolute Deviation (MAD)1.2896
Skewness1.0116745
Sum1916.2016
Variance4.4426398
MonotonicityNot monotonic
2026-02-23T01:12:45.153175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49525
 
1.0%
5.72094
 
0.8%
5.28734
 
0.8%
5.40074
 
0.8%
6.81474
 
0.8%
6.06223
 
0.6%
6.33613
 
0.6%
7.3093
 
0.6%
6.4983
 
0.6%
3.94543
 
0.6%
Other values (401)469
92.9%
ValueCountFrequency (%)
1.12961
0.2%
1.1371
0.2%
1.16911
0.2%
1.17421
0.2%
1.17811
0.2%
1.20241
0.2%
1.28521
0.2%
1.31631
0.2%
1.32161
0.2%
1.33251
0.2%
ValueCountFrequency (%)
12.12651
0.2%
10.71032
0.4%
10.58572
0.4%
9.22291
0.2%
9.22032
0.4%
9.18761
0.2%
9.08921
0.2%
8.90672
0.4%
8.79212
0.4%
8.69661
0.2%

1
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5663366
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:45.268879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7075532
Coefficient of variation (CV)0.9102286
Kurtosis-0.87298988
Mean9.5663366
Median Absolute Deviation (MAD)2
Skewness1.0027438
Sum4831
Variance75.821484
MonotonicityNot monotonic
2026-02-23T01:12:45.329549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24132
26.1%
5115
22.8%
4110
21.8%
338
 
7.5%
626
 
5.1%
224
 
4.8%
824
 
4.8%
119
 
3.8%
717
 
3.4%
ValueCountFrequency (%)
119
 
3.8%
224
 
4.8%
338
 
7.5%
4110
21.8%
5115
22.8%
626
 
5.1%
717
 
3.4%
824
 
4.8%
24132
26.1%
ValueCountFrequency (%)
24132
26.1%
824
 
4.8%
717
 
3.4%
626
 
5.1%
5115
22.8%
4110
21.8%
338
 
7.5%
224
 
4.8%
119
 
3.8%

296.0
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.45941
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:45.449412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.62999
Coefficient of variation (CV)0.41284394
Kurtosis-1.1467628
Mean408.45941
Median Absolute Deviation (MAD)73
Skewness0.6667996
Sum206272
Variance28436.074
MonotonicityNot monotonic
2026-02-23T01:12:45.612108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666132
26.1%
30740
 
7.9%
40330
 
5.9%
43715
 
3.0%
30414
 
2.8%
26412
 
2.4%
39812
 
2.4%
38411
 
2.2%
27711
 
2.2%
33010
 
2.0%
Other values (56)218
43.2%
ValueCountFrequency (%)
1871
 
0.2%
1887
1.4%
1938
1.6%
1981
 
0.2%
2165
1.0%
2227
1.4%
2235
1.0%
22410
2.0%
2261
 
0.2%
2339
1.8%
ValueCountFrequency (%)
7115
 
1.0%
666132
26.1%
4691
 
0.2%
43715
 
3.0%
4329
 
1.8%
4303
 
0.6%
4221
 
0.2%
4112
 
0.4%
40330
 
5.9%
4022
 
0.4%

15.30
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.461782
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:45.727761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.1
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.16252
Coefficient of variation (CV)0.11713495
Kurtosis-0.26706162
Mean18.461782
Median Absolute Deviation (MAD)1.1
Skewness-0.80914507
Sum9323.2
Variance4.6764928
MonotonicityNot monotonic
2026-02-23T01:12:45.849998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2140
27.7%
14.734
 
6.7%
2127
 
5.3%
17.823
 
4.6%
19.219
 
3.8%
17.418
 
3.6%
19.117
 
3.4%
18.617
 
3.4%
16.616
 
3.2%
18.416
 
3.2%
Other values (36)178
35.2%
ValueCountFrequency (%)
12.63
 
0.6%
1312
 
2.4%
13.61
 
0.2%
14.41
 
0.2%
14.734
6.7%
14.83
 
0.6%
14.94
 
0.8%
15.11
 
0.2%
15.213
 
2.6%
15.32
 
0.4%
ValueCountFrequency (%)
222
 
0.4%
21.215
 
3.0%
21.11
 
0.2%
2127
 
5.3%
20.911
 
2.2%
20.2140
27.7%
20.15
 
1.0%
19.78
 
1.6%
19.68
 
1.6%
19.219
 
3.8%

396.90
Real number (ℝ)

Distinct357
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.59438
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:45.970037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.362
Q1375.33
median391.43
Q3396.21
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.88

Descriptive statistics

Standard deviation91.367787
Coefficient of variation (CV)0.2562233
Kurtosis7.2043909
Mean356.59438
Median Absolute Deviation (MAD)5.47
Skewness-2.8867466
Sum180080.16
Variance8348.0725
MonotonicityNot monotonic
2026-02-23T01:12:46.123245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9120
 
23.8%
395.243
 
0.6%
393.743
 
0.6%
395.582
 
0.4%
393.232
 
0.4%
376.142
 
0.4%
389.712
 
0.4%
395.562
 
0.4%
395.62
 
0.4%
390.942
 
0.4%
Other values (347)365
72.3%
ValueCountFrequency (%)
0.321
0.2%
2.521
0.2%
2.61
0.2%
3.51
0.2%
3.651
0.2%
6.681
0.2%
7.681
0.2%
9.321
0.2%
10.481
0.2%
16.451
0.2%
ValueCountFrequency (%)
396.9120
23.8%
396.421
 
0.2%
396.331
 
0.2%
396.31
 
0.2%
396.281
 
0.2%
396.241
 
0.2%
396.231
 
0.2%
396.212
 
0.4%
396.141
 
0.2%
396.062
 
0.4%

4.98
Real number (ℝ)

High correlation 

Distinct454
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.668257
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:46.232826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.706
Q17.01
median11.38
Q316.96
95-th percentile26.81
Maximum37.97
Range36.24
Interquartile range (IQR)9.95

Descriptive statistics

Standard deviation7.1399504
Coefficient of variation (CV)0.56360951
Kurtosis0.49197547
Mean12.668257
Median Absolute Deviation (MAD)4.81
Skewness0.9047527
Sum6397.47
Variance50.978891
MonotonicityNot monotonic
2026-02-23T01:12:46.325230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.363
 
0.6%
8.053
 
0.6%
14.13
 
0.6%
7.793
 
0.6%
18.133
 
0.6%
23.982
 
0.4%
8.12
 
0.4%
3.762
 
0.4%
6.722
 
0.4%
18.062
 
0.4%
Other values (444)480
95.0%
ValueCountFrequency (%)
1.731
0.2%
1.921
0.2%
1.981
0.2%
2.471
0.2%
2.871
0.2%
2.881
0.2%
2.941
0.2%
2.961
0.2%
2.971
0.2%
2.981
0.2%
ValueCountFrequency (%)
37.971
0.2%
36.981
0.2%
34.771
0.2%
34.411
0.2%
34.371
0.2%
34.021
0.2%
31.991
0.2%
30.812
0.4%
30.631
0.2%
30.621
0.2%

24.00
Real number (ℝ)

High correlation 

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.529901
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2026-02-23T01:12:46.408289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117
median21.2
Q325
95-th percentile43.42
Maximum50
Range45
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.2059912
Coefficient of variation (CV)0.40861215
Kurtosis1.4881935
Mean22.529901
Median Absolute Deviation (MAD)4
Skewness1.1080358
Sum11377.6
Variance84.750275
MonotonicityNot monotonic
2026-02-23T01:12:46.503747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5016
 
3.2%
258
 
1.6%
23.17
 
1.4%
227
 
1.4%
21.77
 
1.4%
19.46
 
1.2%
20.66
 
1.2%
23.95
 
1.0%
19.35
 
1.0%
21.25
 
1.0%
Other values (219)433
85.7%
ValueCountFrequency (%)
52
0.4%
5.61
 
0.2%
6.31
 
0.2%
72
0.4%
7.23
0.6%
7.41
 
0.2%
7.51
 
0.2%
8.11
 
0.2%
8.32
0.4%
8.42
0.4%
ValueCountFrequency (%)
5016
3.2%
48.81
 
0.2%
48.51
 
0.2%
48.31
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
44.81
 
0.2%
441
 
0.2%
43.81
 
0.2%

Interactions

2026-02-23T01:12:41.819573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:27.094350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:28.176641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:29.594241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:30.869603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:32.263449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:33.308240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:34.360706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:35.264419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:36.573912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:38.028893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:39.193680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:40.490188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:41.890127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:27.171294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:28.260809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:29.693801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:30.987637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-23T01:12:35.018534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:36.265190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:37.721951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:38.934106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:40.147888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:41.514599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:42.849304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:27.982179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:29.377227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:30.657030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:32.037850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:33.159898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:34.217986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:35.087599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:36.372061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:37.832622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:39.013798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:40.262098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:41.625803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:42.952131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:28.066731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:29.496827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:30.760728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:32.137848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:33.229547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:34.287676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:35.161658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:36.478110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:37.934401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:39.102020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:40.407504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-23T01:12:41.725313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-23T01:12:46.584388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
00.006320.5380115.3018.002.31024.00296.0396.904.09004.986.575065.20
01.0000.0000.1750.1310.1580.0260.1450.2100.0410.0520.0860.0000.0040.000
0.006320.0001.0000.8240.7260.464-0.5700.734-0.5590.729-0.358-0.7460.633-0.3070.705
0.53800.1750.8241.0000.5880.393-0.6360.793-0.5630.650-0.297-0.8800.638-0.3100.795
10.1310.7260.5881.0000.316-0.2760.453-0.3460.705-0.280-0.4960.392-0.1050.418
15.300.1580.4640.3930.3161.000-0.4470.432-0.5560.453-0.070-0.3220.466-0.3110.356
18.000.026-0.570-0.636-0.276-0.4471.000-0.6410.437-0.3710.1610.614-0.4880.360-0.544
2.3100.1450.7340.7930.4530.432-0.6411.000-0.5780.665-0.283-0.7580.637-0.4140.680
24.000.210-0.559-0.563-0.346-0.5560.437-0.5781.000-0.5620.1850.446-0.8530.633-0.548
296.00.0410.7290.6500.7050.453-0.3710.665-0.5621.000-0.329-0.5750.534-0.2710.526
396.900.052-0.358-0.297-0.280-0.0700.161-0.2830.185-0.3291.0000.249-0.2080.052-0.228
4.09000.086-0.746-0.880-0.496-0.3220.614-0.7580.446-0.5750.2491.000-0.5640.263-0.801
4.980.0000.6330.6380.3920.466-0.4880.637-0.8530.534-0.208-0.5641.000-0.6400.658
6.57500.004-0.307-0.310-0.105-0.3110.360-0.4140.633-0.2710.0520.263-0.6401.000-0.278
65.200.0000.7050.7950.4180.356-0.5440.680-0.5480.526-0.228-0.8010.658-0.2781.000

Missing values

2026-02-23T01:12:43.111660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-23T01:12:43.196765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

0.0063218.002.31000.53806.575065.204.09001296.015.30396.904.9824.00
00.027310.07.0700.4696.42178.94.96712242.017.8396.909.1421.6
10.027290.07.0700.4697.18561.14.96712242.017.8392.834.0334.7
20.032370.02.1800.4586.99845.86.06223222.018.7394.632.9433.4
30.069050.02.1800.4587.14754.26.06223222.018.7396.905.3336.2
40.029850.02.1800.4586.43058.76.06223222.018.7394.125.2128.7
50.0882912.57.8700.5246.01266.65.56055311.015.2395.6012.4322.9
60.1445512.57.8700.5246.17296.15.95055311.015.2396.9019.1527.1
70.2112412.57.8700.5245.631100.06.08215311.015.2386.6329.9316.5
80.1700412.57.8700.5246.00485.96.59215311.015.2386.7117.1018.9
90.2248912.57.8700.5246.37794.36.34675311.015.2392.5220.4515.0
0.0063218.002.31000.53806.575065.204.09001296.015.30396.904.9824.00
4950.289600.09.6900.5855.39072.92.79866391.019.2396.9021.1419.7
4960.268380.09.6900.5855.79470.62.89276391.019.2396.9014.1018.3
4970.239120.09.6900.5856.01965.32.40916391.019.2396.9012.9221.2
4980.177830.09.6900.5855.56973.52.39996391.019.2395.7715.1017.5
4990.224380.09.6900.5856.02779.72.49826391.019.2396.9014.3316.8
5000.062630.011.9300.5736.59369.12.47861273.021.0391.999.6722.4
5010.045270.011.9300.5736.12076.72.28751273.021.0396.909.0820.6
5020.060760.011.9300.5736.97691.02.16751273.021.0396.905.6423.9
5030.109590.011.9300.5736.79489.32.38891273.021.0393.456.4822.0
5040.047410.011.9300.5736.03080.82.50501273.021.0396.907.8811.9